38 research outputs found

    Relationship between visual field loss and maximal combined electroretinographic responses in retinitis pigmentosa : comparison among genetically different types

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    1984年から1996年までに千葉大学眼科を受診した定型網膜色素変性症228例について常優26例, 常劣64例, 孤発例138例に分け動的量的視野および網膜電図を検討した。Goldmann視野におけるV-4イソプターでは5から10cm^2までと150から250cm^2までの2群に別れ, 1-4イソプターでは5cm^2以下の群のみ認められた。加齢及び遺伝形式による差異は認められなかった。網膜電図ではa, b波ともに正常対照群より減少してはいるものの, 遺伝形式による差異は認められなかった。また, 網膜電図で振幅を認められる割合はV-4イソプターおよびI-4イソプターの面積と相関していることが示唆されたが, 遺伝形式による差異は認められなかった。網膜電図の振幅の比であるb/a比は正常対象群に対して疾患群は減少していたが, 常備は特に他に比べ有意に減少していた。定型網膜色素変性症の網膜電図や視野の検討は数多くなされてきたが, b/a比について統計的考察がなされてきたことはない。a波およびb波は組織学的に発生起源が異なっており, b波はa波のインパルスによって二次的に引き起こされることは以前より知られてきている。網膜電図において常備のb/a比が有意な低下を示すことは, 網膜障害の機序が他と異なる可能性が示唆された。Analyses were performed on 228 Japanese patients with retinitis pigmentosa (RP) who were classified with autosomal dominant (ADRP, n=26), autosomal recessive (ARRP, n=64), and simplex (simplex RP, rc=138) inheritance. Visual fields were tested with Goldmann perimetry. Maximal combined responses of electroretinogram (ERG) with 20-Joule white flash stimulation were recorded after dark adaptation for 20 minutes. The visual field with the V-4 isopter demonstrated two unique groups, represented by dense areas between 5 and 10cm^2 and between 150 and 250cm^2, while only one unique group was observed within the 5cm^2 area with the 1-4 isopter. No age or inheritance type of effect was seen. A-and b-wave amplitudes were equally low in the 3 groups, as compared with normal subjects. The b/a ratio was significantly smaller in the ADRP group, compared with the others. The rate of detectable ERG responses decreased as the visual field became smaller. There was no inheritance effect. A lower b/a ratio in ADRP patients suggested that retinal functional abnormalities differed from ARRP and simplex RP patients

    Meta-Analytic Framework for Sparse <i>K</i>-Means to Identify Disease Subtypes in Multiple Transcriptomic Studies

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    <p>Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step toward this goal. In this article, we extend a sparse <i>K</i>-means method toward a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer datasets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. Supplementary materials for this article are available online.</p

    Additional file 1: Table S1. of Non-coding single nucleotide variants affecting estrogen receptor binding and activity

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    The list of all ER ChIP-seq datasets in breast cancer. Table S2. List of E2-regulated genes common in vitro, in vivo, and TCGA. Table S3. List of the studies used for the identification of E2-regulated genes. Table S4. Primer sets used for different assays. Table S5. The list of regulatory SNVs in MCF7 Cell line. Table S6. The list of regulatory SNVs in BT474 cell line. Table S7. The list of regulatory SNVs in MDA-MB-134 cell line. Table S8. The list of regulatory SNVs in T47D cell line. Table S9. The list of regulatory SNVs in TAMR cell line. Table S10. The list of regulatory SNVs in ZR75 cell line. Table S11. The list of regulatory SNVs in good prognosis tumors. Table S12. The list of regulatory SNVs in bad prognosis tumors. Table S13. The list of regulatory SNVs in metastatic tumors. Table S14. The allele frequency of top RegSNVs in ER-binding sites with sufficient coverage. (XLSX 3641 kb

    Additional file 2: Figure S1. of Non-coding single nucleotide variants affecting estrogen receptor binding and activity

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    The UCSC gnome browser view of the second intron in IGF1R gene. The index SNP, rs62022087, seems to be located in a region bound by several chromatin-modifying factors based on ENCODE data. Figure S2. The visualization of ChIP-seq reads from multiple cell lines over rs62022087 SNP site in two individual studies: (A) Hurtado et al. {Hurtado, 2011 #23}, (B) Joseph et al. {Joseph, 2010 #15}. Figure S3. The distribution of RegSNVs over the gnome across a panel of breast cancer cell lines, good and bad prognosis tumors. The binding sites from different ER ChIP-seq datasets were extracted and annotated based on their location in the genome. The majority of the binding sites are located in the intergenic and intronic areas. (DOCX 384 kb

    UMAP of Celligner alignment between tumors and PDX/PDO models.

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    (A) Three distinct clusters were observed. The small cluster on the left consists of a seemingly rare breast cancer subtype, the upper-right cluster includes mostly non-basal samples, and the lower-right cluster includes mostly basal samples. (B) UMAP is redrawn when the small cluster in (A) is removed. (TIF)</p

    Pathway- and gene-specific analysis for selection of representative cell line(s).

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    (A) Heatmap of pathway-specific deviance scores (DSpath) with 14 unbiased-selected and 1 manually-included pathways (30 size NES| > 1.5; shown on the rows) and 9 unbiased-selected and 1 manually-included cell lines (columns). The genome-wide SDA projected deviance score (DSSDA) is shown on the top side-bar and the pathway size and normalized enrichment score (NES) are on the left. Positive (negative) NES indicates up-regulation (down-regulation) in ILC compared to IDC. Average of the 14 pathways and the pre-selected “KEGG Cell Adhesion Molecules” pathway are shown at the bottom. The p-values of DSpath are annotated in the heatmap (one circle: p − value p − value p − value DSgene for the 10 selected cell lines and 22 DE genes in “KEGG Cell Adhesion Molecules” pathway. (C) Part of KEGG PathView topological networks for BCK4 (DSpath = 1.323) for the “KEGG Cell Adhesion Molecules” pathway. The result shows discordance of 10 genes in BCK4 (orange stars showing up-regulation compared to ILC tumors and blue start showing down-regulation).</p

    Selecting representative PDO/PDX for ILC.

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    (A) SDA projected positions for PDO and PDX models from PDMR. Four models (three PDXs and one PDO; red circles) from the same patient (171881–019-R) were identified as candidate ILC models. Six models from this patient are labeled with the sample ID. High consistency was observed between SDA deviance scores and passages among PDX models. (B) Six models originated from the same patient were used for pathway-specific analysis. Six models show high congruence in the majority of 14 pathways and the Cell Adhesion pathway. (C) Violin plot shows the position of PDO.1 and PDX.1B on the six genes on which PDO.1 is discordant with.</p

    Flowchart of CASCAM for congruence quantification and selection.

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    Tumor and cancer model gene expression data are first harmonized (Module 1). Transparent machine learning by sparse discriminant analysis (SDA) is applied by combining predication accuracy and SDA-based deviance score for pre-selecting candidate cancer models (Module 2). Pathway-specific mechanistic explorations are iteratively investigated to conclude the final representative cancer model (Module 3). Blue frames represent input data, orange frames for essential output results, parallelogram frames for intermediate results, rectangular frames for analysis process, bullet-shaped frames for visualization, and rhombus frames for decision making.</p